A conceptual framework for tactually guided exploration and shape perception

A conceptual framework for tactually guided exploration and shape perception using a robotic medium is provided. The conceptual framework identifies the needed sensory information, the spatial and temporal transformations of this information, the control mechanism, both feedforward and feedback, for performing the missions and the perception machinery. These attributes, in turn, sharpen the focus on the major building blocks needed in design of artificial skins, tactile sensing, and processing systems of the future. These building blocks are identification of central nervous system (CNS) machinery in living systems that perform the required computations, mappings, processors for extraction of the needed manipulation and recognition parameters, processing of outputs of populations of natural tactile sensors, and finally, understanding the dynamics of sensor-imbedded skin and artificial skin in fixed, gliding, and rolling contact with known and unknown objects and surfaces.<<ETX>>

[1]  S. J. Lederman,et al.  Chapter 12 – TACTUAL PERCEPTION OF TEXTURE , 1973 .

[2]  W. Daniel Hillis,et al.  A High-Resolution Imaging Touch Sensor , 1982 .

[3]  Hooshang Hemami,et al.  Modeling of Nonholonomic Dynamic Systems With Applications , 1981 .

[4]  Jia-Yuan Han Stability, contact force and adaptive trajectory control of natural and robotic systems / , 1986 .

[5]  Phillip W. Barth Silicon sensors meet integrated circuits , 1990 .

[6]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .

[7]  A. Vallbo,et al.  Somatosensory, proprioceptive, and sympathetic activity in human peripheral nerves. , 1979, Physiological reviews.

[8]  K. O. Johnson,et al.  Reconstruction of population response to a vibratory stimulus in quickly adapting mechanoreceptive afferent fiber population innervating glabrous skin of the monkey. , 1974, Journal of neurophysiology.

[9]  James A. Anderson,et al.  Cognitive and psychological computation with neural models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Leon D. Harmon,et al.  Automated Tactile Sensing , 1982 .

[11]  Matthew T. Mason,et al.  Mechanics and Planning of Manipulator Pushing Operations , 1986 .

[12]  A. Azulay,et al.  The role of the dorsal funiculus of the primate in tactile discrimination , 1975, Experimental Neurology.

[13]  K O Johnson,et al.  Sensory discrimination: decision process. , 1980, Journal of neurophysiology.

[14]  G. Kinoshita,et al.  Tactile sensor design and tactile sensing on 3-D objects , 1985 .

[15]  Hooshang Hemami,et al.  A mechanism for touch control of a sagittal five-link finger-hand , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Hooshang Hemami Differential surface models for tactile perception of shape and online tracking of features , 1988, IEEE Trans. Syst. Man Cybern..

[17]  A C Sanderson,et al.  Neural modeling and model identification. , 1985, Critical reviews in biomedical engineering.

[18]  C. Marsden,et al.  The sensory mechanism of servo action in human muscle. , 1977, The Journal of physiology.

[19]  Dragan Stokic,et al.  Implementation of Force Feedback in Manipulation Robots , 1986 .

[20]  A. Vallbo,et al.  Activity from skin mechanoreceptors recorded percutaneously in awake human subjects. , 1968, Experimental neurology.

[21]  J. Ko Sensory discrimination: neural processes preceding discrimination decision. , 1980 .

[22]  Hooshang Hemami,et al.  Recognition of geometrical shape by a Robotic Probe , 1987, J. Field Robotics.

[23]  Morris Driels,et al.  Evaluation of a grey scale tactile array sensor pad for robotic applications , 1985, J. Field Robotics.

[24]  H. Fritz,et al.  Tactile force-torque sensor for performing control tasks in robotics , 1986 .

[25]  Ümit Özgüner,et al.  Control of a planar arm by nonlinear feedback gains , 1984, J. Field Robotics.